Yes, sorry I thought it was clear above (in my comment). The only thing that is different is changing the default overlaps for long reads. Otherwise, you can run it just the same as with paired-end data (and the results will be comparable).

I was trying to install your tool, Transposome using the commands given. I successfully installed all the dependencies, but am stuck at the last step of installation. I am copying the commands and error here. I'll appreciate your help,

I tested on Ubuntu 12.04 and it does install under /usr/local on that system if you are using the system Perl. You just need to type "sudo make install" to install it. Otherwise, I suggest setting up perlbrew (it is very easy, and there are copy-and-paste commands to do it on the Transposome wiki under "installing dependencies") so you don't need admin to do anything with Perl.

For some context, genome assembly is complicated by repeats and the regions that are typically missing, compressed, or misassembled are the repetitive regions. Therefore, you don't want to do a low-coverage assembly to look for repeats. If you have a high-quality draft supported by genetic and physical maps, cytogenetic data, etc. then use the assembly. If not, you are going to be telling lies!

RepeatExplorer and Transposome (developed by myself) were both designed around solving problems with plant genomes, so this is an ideal use case. RepeatExplorer underestimates the repeat abundance (sometimes by a lot), so this is something important to consider if you are thinking of making a biological or evolutionary study. On the other hand, it may be easier to use (web vs. command line) depending on your background, albeit much slower. I don't have experience running RepeatExplorer with single-end data, but I can tell you that Transposome seems to do better, in terms of biological expectations, with long reads including single-end, so this should work well. If you have any questions, feel free to ask.

Yes, you can use k-mer frequency to look at repeat properties in the genome (uniqueness, occurrence ratios, etc.) but this is only really informative (biologically) when combined with information about what TEs are in a genome. Without that, you can't say a whole lot based on k-mers alone. This approach is super useful for comparative purposes though, and for visualizing the genomic abundance of repeats (in Fig. 2 of this paper I did this to show the genomic abundance of repeats in a BAC).